ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides.

ABSTRACT:

Motivation

Anti-cancer peptides (ACPs) have recently emerged as promising therapeutic agents for cancer treatment. Due to the avalanche of protein sequence data in the post-genomic era, there is an urgent need to develop automated computational methods to enable fast and accurate identification of novel ACPs within the vast number of candidate proteins and peptides.

Results

To address this, we propose a novel predictor named Anti-Cancer peptide Predictor with Feature representation Learning (ACPred-FL) for accurate prediction of ACPs based on sequence information. More specifically, we develop an effective feature representation learning model, with which we can extract and learn a set of informative features from a pool of support vector machine-based models trained using s

SUBMITTER: Wei L 

PROVIDER: S-EPMC6247924 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature